Finance
Balancing the books – how process mining can turbo-charge internal auditing
Published by Gbaf News
Posted on May 16, 2018
8 min readLast updated: January 21, 2026
Global Banking and Finance Review
Company
Published by Gbaf News
Posted on May 16, 2018
8 min readLast updated: January 21, 2026
Bastian Nominacher, co-founder and co-CEO, Celonis
For years, auditors have used interviews and workshops to conduct successful company assessments. As more businesses become digitised, auditors need to blend traditional techniques with a more data-led approach. In the face of higher expectations for accuracy, internal departments need to take note: big data is changing the way organisations operate and internal audit departments can’t afford to be left behind. However, PwC’s 2018 State of the Internal Audit Profession study showed that only 14 percent of audit professionals are advanced in their adoption of technology – clearly there is still progress to be made.
As business processes become more complex and greater volumes of information become available, auditors are under pressure to use analytics to turn assets into insights. This means understanding the inner workings of those processes instead of basing audits on a small data sample. But how can internal audit teams capitalise on the wealth of data available – from their own business activities as well as external sources – to increase efficiency?
Helping auditors drive business change
Despite the promises of data-driven methods, integrating technology into the audit process has traditionally proved difficult, given that it still relies on obtaining relevant information. However, internal teams should theoretically have the information more readily to hand than external contractors tasked with collating requests from around a business. Therefore, analytics has become a key facet of internal audit strategy, by freeing up auditors to manage risk more proactively, rather than focusing on data aggregation.
Traditional methods have helped audit teams eliminate many manual errors. Conducting an audit based on more accurate, real-time information can help streamline approvals, timelines, exceptions and violations in order to improve processes and avoid risks. As more advanced tools such as automated data capture enter the arsenal of the auditor, internal departments can analyse swathes of unstructured data such as contracts and trading information. Notably, this can help identify fraudulent activity that needs investigating. More generally, it enables focus on higher risk areas rather than arbitrarily sampling areas of a business.
It’s all in the process
However, the complexity of the modern business environment requires an even more advanced approach. For example, techniques such as process mining use the digital event logs in a company’s existing IT system to create a visual reconstruction of the business processes across an organisation. In the context of auditing, this allows the department to visualise and analyse aspects such as purchase-to-pay, order-to-cash, production and logistics, to achieve complete transparency into how these processes are working.
Machine learning and artificial intelligence algorithms are also playing an increasingly important role in this, by guiding the auditor through the analysis and pointing out compliance issues and inefficiencies along the way. Simply put, the combination of analytics and AI acts like a personal MRI machine, providing unbiased visibility and diagnostics of an organisation’s processes.
This level of advanced analytics can support the internal auditing process from preparation through to reporting, as it helps benchmark process execution to set the right focus for the audit upfront. For example, a system containing a significant volume of un-approved purchase orders can experience real problems in the purchase-to-pay process. As the approval of purchase orders is key from a compliance perspective, visibility over all purchase orders is vital for an efficient auditing department.
Examining processes under a microscope in this way can uncover hidden bottlenecks and reduce compliance costs, as well as the cost of auditing itself. This level of visibility can also help with broader business challenges, such as integrating multiple systems following a merger or acquisition.
Creating a vision for audit
Internal teams shouldn’t be afraid to move beyond traditional analytics to transform auditing as we know it. Simplifying processes, automating mundane tasks, and transforming operations are just the beginning. With the ability to proactively monitor issues that could result in significant business risk, auditors can position themselves as digital leaders within their organisations.
The complexity and rapid evolution of modern business require equally capable solutions. There’s no denying it – big data has transformed the way organisations operate, and internal audit departments shouldn’t suffer as other teams innovate. Auditors need to ensure they employ the best methods to achieve high precision and full process transparency to avoid getting left behind.
Bastian Nominacher, co-founder and co-CEO, Celonis
For years, auditors have used interviews and workshops to conduct successful company assessments. As more businesses become digitised, auditors need to blend traditional techniques with a more data-led approach. In the face of higher expectations for accuracy, internal departments need to take note: big data is changing the way organisations operate and internal audit departments can’t afford to be left behind. However, PwC’s 2018 State of the Internal Audit Profession study showed that only 14 percent of audit professionals are advanced in their adoption of technology – clearly there is still progress to be made.
As business processes become more complex and greater volumes of information become available, auditors are under pressure to use analytics to turn assets into insights. This means understanding the inner workings of those processes instead of basing audits on a small data sample. But how can internal audit teams capitalise on the wealth of data available – from their own business activities as well as external sources – to increase efficiency?
Helping auditors drive business change
Despite the promises of data-driven methods, integrating technology into the audit process has traditionally proved difficult, given that it still relies on obtaining relevant information. However, internal teams should theoretically have the information more readily to hand than external contractors tasked with collating requests from around a business. Therefore, analytics has become a key facet of internal audit strategy, by freeing up auditors to manage risk more proactively, rather than focusing on data aggregation.
Traditional methods have helped audit teams eliminate many manual errors. Conducting an audit based on more accurate, real-time information can help streamline approvals, timelines, exceptions and violations in order to improve processes and avoid risks. As more advanced tools such as automated data capture enter the arsenal of the auditor, internal departments can analyse swathes of unstructured data such as contracts and trading information. Notably, this can help identify fraudulent activity that needs investigating. More generally, it enables focus on higher risk areas rather than arbitrarily sampling areas of a business.
It’s all in the process
However, the complexity of the modern business environment requires an even more advanced approach. For example, techniques such as process mining use the digital event logs in a company’s existing IT system to create a visual reconstruction of the business processes across an organisation. In the context of auditing, this allows the department to visualise and analyse aspects such as purchase-to-pay, order-to-cash, production and logistics, to achieve complete transparency into how these processes are working.
Machine learning and artificial intelligence algorithms are also playing an increasingly important role in this, by guiding the auditor through the analysis and pointing out compliance issues and inefficiencies along the way. Simply put, the combination of analytics and AI acts like a personal MRI machine, providing unbiased visibility and diagnostics of an organisation’s processes.
This level of advanced analytics can support the internal auditing process from preparation through to reporting, as it helps benchmark process execution to set the right focus for the audit upfront. For example, a system containing a significant volume of un-approved purchase orders can experience real problems in the purchase-to-pay process. As the approval of purchase orders is key from a compliance perspective, visibility over all purchase orders is vital for an efficient auditing department.
Examining processes under a microscope in this way can uncover hidden bottlenecks and reduce compliance costs, as well as the cost of auditing itself. This level of visibility can also help with broader business challenges, such as integrating multiple systems following a merger or acquisition.
Creating a vision for audit
Internal teams shouldn’t be afraid to move beyond traditional analytics to transform auditing as we know it. Simplifying processes, automating mundane tasks, and transforming operations are just the beginning. With the ability to proactively monitor issues that could result in significant business risk, auditors can position themselves as digital leaders within their organisations.
The complexity and rapid evolution of modern business require equally capable solutions. There’s no denying it – big data has transformed the way organisations operate, and internal audit departments shouldn’t suffer as other teams innovate. Auditors need to ensure they employ the best methods to achieve high precision and full process transparency to avoid getting left behind.
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